By George Denlinger, President of Technology Talent Solutions, Robert Half
It took almost no time for AI to become part of the hiring process—creating efficiencies while also adding new complexity. In a Robert Half survey, 91% of technology leaders say AI recruitment tools have improved hiring speed and candidate quality. But 68% report they’ve encountered challenges with AI-generated resumes and job application materials, including:
Increased need for in-person interviews or additional screening to validate candidates’ skills and experience
Higher application volume from unqualified candidates
More difficulty distinguishing between AI-generated and authentic resumes
Many technology professionals know how to use generative AI tools well, including to develop resumes that closely match employers’ job descriptions. While candidates should put their best foot forward when applying for roles, using AI to create highly polished job application materials can sometimes result in inflating skills and experience.
This is why hiring managers for tech teams should take extra steps to learn how well candidates understand the work, what their specific contributions were in past roles and what changed because of their efforts. They also need to do this efficiently, so they don’t drag out the hiring process and risk losing top contenders to competitors amid an ongoing tech talent shortage.
A framework for tech candidate evaluation: depth, ownership and impact
Verifying a candidate’s technical ability is typically a routine step when recruiting tech and IT talent. But with AI in the hiring process, it has become even more important for employers to be thorough. A resume may state that a candidate worked with a certain system, used a cybersecurity platform or contributed to an AI initiative. But the real question is: What did that person actually do—and what value did they deliver?
To find the answer, hiring managers should focus on 3 key areas when evaluating potential hires:
Depth: How well does the candidate understand the technology, process or platform they listed on their resume, and how does that experience relate to the role they’re applying for?
Ownership: What part of the work did the candidate personally lead, build, support or improve?
Impact: What changed because of their contribution?
Exposure to a range of tools and systems is valuable, especially for early-career professionals. But exposure isn’t the same as applied skill. Candidates should be able to connect the technologies listed on their resume to real work they’ve performed.
Hiring managers should also look for signs of progression, measurable impact and role-specific detail. For example, a resume that says a candidate “supported a cloud migration” is less telling than one that explains which systems were involved, what parts of the project the candidate owned, and how the work improved performance, cost, reliability or security.
The interview is where hiring managers can explore those details further. Behavioral and situational questions can help them understand how candidates think through problems, apply technical knowledge and collaborate in real work settings. Questions might include:
Tell me about a project where you used this technology. What was your role?
What decisions were you responsible for?
What obstacles came up, and how did you address them?
How did the work affect the business, users, team or system performance?
What would you do differently if you handled the project again?
Explore more examples of must-ask interview questions to evaluate potential tech hires.
Keep skills validation focused and relevant
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Additional screening of candidates may be needed in the AI era, but it shouldn’t become a barrier that discourages qualified—or high-potential—professionals from applying. It also shouldn’t create a lengthy hiring process that results in missed opportunities to secure top candidates, especially amid the ongoing tech talent shortage. Robert Half research shows 61% of tech leaders say it’s more challenging to find skilled talent than a year ago.
Employers should keep skills validation focused and relevant. The goal is to confirm a candidate’s ability in a way that supports better hiring decisions. Depending on the role, the process may include a live technical discussion, coding exercise, systems-design conversation or scenario-based interview.
Whatever the format, skills validation should help provide answers to these core questions:
Does the candidate understand the work? (Depth)
Have they held similar responsibilities? (Ownership)
Can they explain how their contributions made a difference? (Impact)
For senior roles, the best assessment may not be a skills test at all. A deeper conversation with candidates about strategy, trade-offs, decision making and leadership may provide more useful insight.
Research for Robert Half’s 2026 tech priorities and opportunities report found that 93% of tech leaders lack the staff and skills needed to achieve strategic priorities this year.
The need to balance AI efficiency with human judgment
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AI can make the hiring process more efficient for candidates and employers alike. Candidates can use generative AI to prepare more tailored resumes and apply to more opportunities faster. Employers can use AI recruitment tools to identify potential candidate matches, manage high volumes of applications and support faster screening. But AI can’t fully replace human judgment in the hiring process.
That’s especially true in the tech field. Career paths in technology aren’t always linear. A candidate may have built relevant skills through adjacent roles, project work, system migrations, cross-functional initiatives or hands-on experience with emerging tools. And as businesses adopt AI, automate workflows and modernize systems, many tech and IT roles are changing quickly.
This is where the IT skills gap becomes even more evident, because it isn’t just technical. While employers need candidates who can immediately step in to work with specific technologies, they also need professionals who can learn quickly, apply sound judgment and understand how their contributions connect to business goals. Soft skills are particularly vital in the AI era, and data literacy is emerging as a core capability for AI initiatives as well.
As recruiting and evaluating tech talent becomes more complex and AI continues to change both sides of the hiring process, many employers are actively seeking support. In a Robert Half survey, 70% of technology leaders say the rise of AI has made them more likely to turn to a staffing or consulting firm for help in navigating tech hiring. Top reasons for using this strategy include:
Finding candidates with specialized AI skills
Advising on AI-related workforce planning and identifying skills gaps
Managing higher volumes of AI-generated job applications
Working with experienced recruiters can help reduce the time employers spend searching for skilled tech talent in a challenging labor market. In fact, 93% of tech leaders rate staffing firms effective in solving AI-related hiring challenges. Specialized recruiters can connect faster with candidates whose skills and experience have already been reviewed and vetted. They can immediately understand a technology professional’s depth, ownership and impact—and help you hire that candidate with more confidence.
